The continuous shift of shoreline boundaries due to natural or anthropogenic events has created the necessity to monitor the shoreline boundaries regularly. This study investigates the perspective of implementing artificial intelligence techniques to model and predict the realignment in shoreline along the eastern Indian coast of Orissa (now called Odisha). The modeling consists of analyzing the satellite images and corresponding reanalysis data of the coastline. The satellite images (Landsat imagery) of the Orissa coastline were analyzed using edge detection filters, mainly Sobel and Canny. Sobel and canny filters use edge detection techniques to extract essential information from satellite images. Edge detection reduces the volume of data and filters out worthless information while securing significant structural features of satellite images. The image differencing technique is used to determine the shoreline shift from GIS images (Landsat imagery). The shoreline shift dataset obtained from the GIS image is used together with the metrological dataset extracted from Modern-Era Retrospective analysis for Research and Applications, Version 2, and tide and wave parameter obtained from the European Centre for Medium-Range Weather Forecast for the period 1985–2015, as input parameter in machine learning (ML) algorithms to predict the shoreline shift. Artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machine (SVM) algorithm are used as a ML model in the present study. The ML model contains weights that are multiplied with relevant inputs/features to obtain a better prediction. The analysis shows wind speed and wave height are the most prominent features in shoreline shift prediction. The model’s performance was compared, and the observed result suggests that the ANN model outperforms the KNN and SVM model with an accuracy of 86.2%.

中文翻译：

---

使用机器学习绘制海岸线变化图：来自印度东部沿海的案例研究

由于自然或人为事件造成的海岸线边界的不断变化，使得有必要定期监测海岸线边界。这项研究调查了实施人工智能技术来建模和预测印度东部奥里萨邦沿海（现在称为奥里萨邦）海岸线重新排列的前景。建模包括分析卫星图像和海岸线的相应再分析数据。使用边缘检测滤镜（主要是Sobel和Canny）分析了Orissa海岸线的卫星图像（Landsat图像）。Sobel和Canny滤镜使用边缘检测技术从卫星图像中提取基本信息。边缘检测可减少数据量并过滤出毫无价值的信息，同时确保卫星图像的重要结构特征。图像差分技术用于确定GIS图像（Landsat图像）的海岸线偏移。从GIS图像获得的海岸线平移数据集与从现代时代研究和应用回顾性分析第二版中提取的计量数据集以及从欧洲中距离天气预报中心获得的潮汐和波浪参数一起使用1985-2015年，作为机器学习（ML）算法的输入参数来预测海岸线的变化。本文将人工神经网络（ANN），k最近邻（KNN）和支持向量机（SVM）算法用作ML模型。ML模型包含权重，再乘以相关的输入/特征以获得更好的预测。分析表明，风速和波高是海岸线偏移预测中最突出的特征。比较了模型的性能，观察结果表明，ANN模型优于KNN和SVM模型，准确度为86.2％。